Modita Rana — Software Engineer

Backend and observability engineer with ~5 years of experience building scalable APIs, telemetry pipelines, distributed systems, and cloud-native platform components across observability, analytics, and energy domains. Currently working at Akamai Technologies as a Software Engineer II, contributing to the Linode Observability Platform through development of the Metric Read (MR) API and telemetry ingestion systems. Primary engineer driving MR API feature development, architecture evolution, deployment workflows, operational stability, and production troubleshooting. Working extensively with Go, Python, OpenTelemetry, Prometheus, Kubernetes, and distributed observability pipelines. Contributing to custom OpenTelemetry Collector receivers, telemetry aggregation workflows, and scalable cloud resource discovery systems integrated with Linode APIs. Also involved in observability dashboards and internal tooling supporting telemetry visualization and operational monitoring. Previously at Saras Analytics, worked on Daton’s microservices platform, building event-driven systems using Kafka, Spring Boot, and event sourcing architectures with strong focus on resiliency, monitoring, and scalability. Started career at GridX (formerly Innowatts), developing AI-driven forecasting pipelines and real-time streaming systems on AWS using Kafka, Spark, EMR, and large-scale ETL workflows. Currently pursuing M.Tech in Cloud Computing from Indian Institute of Technology Patna with focus on distributed systems and cloud-native architectures.

Stackforce AI infers this person is a Backend Engineer specializing in SaaS and Energy sectors with a focus on observability and data engineering.

Location: Bengaluru, Karnataka, India

Experience: 4 yrs 7 mos

Skills

Distributed Systems
Software Observability
Microservices
Full-stack Development
Data Engineering
Backend Development

Career Highlights

Expert in building scalable APIs and telemetry pipelines.
Strong background in distributed systems and cloud-native architectures.
Proven track record in enhancing observability and operational stability.

Work Experience

Akamai Technologies

Software Engineer 2 (1 yr 8 mos)

Saras Analytics

Software Development Engineer (1 yr 4 mos)

Innowatts

Back End Engineer (1 yr 7 mos)

NHPC Limited

Summer Internship (6 mos)

Education

Master of Technology - MTech at Indian Institute of Technology, Patna

Bachelor of Technology - BTech at J.C. Bose University of Science and Technology, YMCA

Diploma at Government Polytechnic College

Modita Rana

Software Engineer

Bengaluru, Karnataka, India4 yrs 7 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in building scalable APIs and telemetry pipelines.
Strong background in distributed systems and cloud-native architectures.
Proven track record in enhancing observability and operational stability.

Stackforce AI infers this person is a Backend Engineer specializing in SaaS and Energy sectors with a focus on observability and data engineering.

Contact

Skills

Core Skills

Distributed SystemsSoftware ObservabilityMicroservicesFull-stack DevelopmentData EngineeringBackend Development

Other Skills

LinuxSystem ValidationGo (Programming Language)PythonKubernetesOpenTelemetryPrometheusGrafanaGoogle Cloud Platform (GCP)RedisSpring BootKafkaReactTypeScriptApache Airflow

About

Experience

4 yrs 7 mos

Total Experience

1 yr 6 mos

Average Tenure

1 yr 8 mos

Current Experience

Akamai technologies

Software Engineer 2

Oct 2024 – Present · 1 yr 8 mos · Bengaluru · Hybrid

Contributing to Akamai’s Linode Observability Platform by driving development and architectural evolution of the Metric Read (MR) API and telemetry ingestion systems powering internal cloud observability workflows.
Primary engineer owning the Metric Read (MR) API end-to-end, including architecture, feature development, deployment, operational stability, and production troubleshooting across the observability stack.
Led the v2 redesign of the MR API, improving query throughput, scalability, reliability, and maintainability for high-volume telemetry workloads.
Designed and implemented split-by capabilities for entity- and node-level observability across Kubernetes clusters and cloud infrastructure resources.
Built scalable backend services in Go and Python for telemetry processing, metric aggregation, and distributed observability workflows integrated with the Linode cloud ecosystem.
Contributed to Akamai’s custom OpenTelemetry Collector receivers for standardized internal metrics ingestion and telemetry integration.
Developed Resource ID Fetcher modules integrating with Linode APIs, using caching and optimized refresh strategies for large-scale cloud resource discovery.
Integrated Prometheus, Grafana, and OpenTelemetry pipelines to enhance visibility into platform health, infrastructure behavior, and query performance.
Worked extensively with Kubernetes and Armada-based deployment environments, handling deployments, runtime debugging, scaling, and operational reliability.
Improved platform resiliency through strong validation layers, telemetry instrumentation, structured error propagation, and production-focused debugging practices.
Collaborated closely with platform and infrastructure teams while serving as the primary technical owner for MR API continuity and feature evolution.

LinuxSystem ValidationGo (Programming Language)PythonKubernetesOpenTelemetry+4

Saras analytics

Software Development Engineer

Feb 2023 – Jun 2024 · 1 yr 4 mos · Hyderabad · Hybrid

Contributed to Daton’s microservice-based data platform by building event-driven, full-stack systems with strong focus on observability, performance, scalability, and data-driven decision-making.
Built an EventStoreDB integration POC using Spring Boot and event sourcing, enabling reliable event storage, versioning, and auditability; exposed REST APIs consumed by React (JSX) admin dashboards.
Designed Kafka-based messaging architecture for inter-service communication, improving throughput and fault tolerance; developed React + TypeScript UI panels with Redux and Context API for real-time monitoring and system visibility.
Implemented JWT-based authentication across microservices and frontend, with Firebase Authentication for secure internal tool access.
Built scalable frontend components using React, TypeScript, Redux, and Context API for data exploration and observability workflows.
Developed backend services using Node.js, ensuring robust API design, validation, and error handling.
Added frontend (Jest) and backend integration tests to improve reliability and reduce regression issues.
Worked with MongoDB for schema design, indexing, and efficient data access patterns.
Delivered end-to-end full stack features across web layers in collaboration with cross-functional teams.
Modeled system metrics using statistical techniques (mean, variance, distribution analysis) to identify anomalies and performance bottlenecks in real time.
Applied probabilistic reasoning (inspired by Bayes’ theorem) to improve error detection and reliability in event-driven workflows.
Represented event streams and metrics as vectors to enable efficient aggregation and transformation using linear algebra concepts.
Applied optimization strategies (inspired by Gradient Descent) to iteratively improve latency, performance, and resource utilization.
Handled numerical stability and floating-point precision issues in metrics computation, ensuring accuracy at scale.

Google Cloud Platform (GCP)RedisSpring BootKafkaReactTypeScript+2

Innowatts

Back End Engineer

Jul 2021 – Feb 2023 · 1 yr 7 mos · Gurugram · Hybrid

Contributed to Innowatts (now Gridx) smart energy platform by building scalable, data-intensive systems for real-time analytics, predictive forecasting, and data-driven decision-making.
Built AI-driven electricity load forecasting pipelines using AWS (S3, EMR) and SQL, processing high-volume energy datasets and applying statistical modeling (mean, variance, distribution analysis) for accurate demand prediction.
Designed and optimized ETL pipelines using Apache Spark (DataFrames) for large-scale ingestion, transformation, and aggregation, improving scalability and performance.
Exposed processed data via REST APIs and integrated with React-based dashboards for energy monitoring and forecasting insights.
Developed internal tools and Python scripts for pipeline monitoring, error handling, and performance tracking, improving operational visibility and reliability.
Applied probabilistic reasoning (inspired by Bayes’ theorem) to handle uncertainty and variability in energy consumption patterns.
Leveraged linear algebra concepts (vectorized representation of time-series data) for efficient computation and transformation of large-scale energy datasets.
Incorporated optimization techniques (inspired by Gradient Descent) to iteratively improve forecasting accuracy and model performance.
Addressed numerical stability and floating-point precision issues in large-scale data processing and aggregation pipelines.
Built full-stack integrations using the MERN stack (MongoDB, Express.js, React, Node.js) to enable interactive visualization and consumption of analytics outputs.
Collaborated with other teams to align backend pipelines with frontend analytics and visualization workflows.
Optimized AWS infrastructure usage by analyzing cloud costs and improving compute efficiency across EMR workloads.
Worked with databases for efficient data storage, indexing, and access patterns supporting both backend processing and frontend consumption.